





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = ['pixel_values']





      def __init__(			self ,		lowercase = True ,		lowercase = None ,		lowercase = 0.9 ,		lowercase = PILImageResampling.BICUBIC ,		lowercase = True ,		lowercase = None ,		lowercase = 1 / 255 ,		lowercase = True ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	None:
            super().__init__(**lowercase					)
            lowerCAmelCase    		=			size if size is not None else {"""shortest_edge""": 224}
            lowerCAmelCase    		=			get_size_dict(lowercase ,		default_to_square=lowercase					)
            lowerCAmelCase    		=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowerCAmelCase    		=			get_size_dict(lowercase ,		param_name="""crop_size"""					)

            lowerCAmelCase    		=			do_resize
            lowerCAmelCase    		=			size
            lowerCAmelCase    		=			crop_pct
            lowerCAmelCase    		=			resample
            lowerCAmelCase    		=			do_center_crop
            lowerCAmelCase    		=			crop_size
            lowerCAmelCase    		=			do_rescale
            lowerCAmelCase    		=			rescale_factor
            lowerCAmelCase    		=			do_normalize
            lowerCAmelCase    		=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            lowerCAmelCase    		=			image_std if image_std is not None else IMAGENET_DEFAULT_STD





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase = PILImageResampling.BICUBIC ,		lowercase = None ,		**lowercase ,		)						->	np.ndarray:
            lowerCAmelCase    		=			get_size_dict(lowercase ,		default_to_square=lowercase					)
            if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                  raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
            if crop_pct is not None:
                  if "shortest_edge" in size:
                        lowerCAmelCase    		=			int(size["""shortest_edge"""] / crop_pct					)
                  elif "height" in size and "width" in size:
                        if size["height"] == size["width"]:
                              lowerCAmelCase    		=			int(size["""height"""] / crop_pct					)
                        else:
                              lowerCAmelCase    		=			(int(size["""height"""] / crop_pct					), int(size["""width"""] / crop_pct					))
                  else:
                        raise ValueError("""Invalid size for resize: {}""".format(lowercase					)					)

                  lowerCAmelCase    		=			get_resize_output_image_size(lowercase ,		size=lowercase ,		default_to_square=lowercase					)
            else:
                  if "shortest_edge" in size:
                        lowerCAmelCase    		=			get_resize_output_image_size(lowercase ,		size=size["""shortest_edge"""] ,		default_to_square=lowercase					)
                  elif "height" in size and "width" in size:
                        lowerCAmelCase    		=			(size["""height"""], size["""width"""])
                  else:
                        raise ValueError("""Invalid size for resize: {}""".format(lowercase					)					)

            return resize(lowercase ,		size=lowercase ,		resample=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase ,		)						->	np.ndarray:
            lowerCAmelCase    		=			get_size_dict(lowercase					)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'					)
            return center_crop(lowercase ,		size=(size["""height"""], size["""width"""]) ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            return rescale(lowercase ,		scale=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase ,		)						->	np.ndarray:
            return normalize(lowercase ,		mean=lowercase ,		std=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = ChannelDimension.FIRST ,		**lowercase ,		)						->	PIL.Image.Image:
            lowerCAmelCase    		=			do_resize if do_resize is not None else self.do_resize
            lowerCAmelCase    		=			crop_pct if crop_pct is not None else self.crop_pct
            lowerCAmelCase    		=			resample if resample is not None else self.resample
            lowerCAmelCase    		=			do_center_crop if do_center_crop is not None else self.do_center_crop
            lowerCAmelCase    		=			do_rescale if do_rescale is not None else self.do_rescale
            lowerCAmelCase    		=			rescale_factor if rescale_factor is not None else self.rescale_factor
            lowerCAmelCase    		=			do_normalize if do_normalize is not None else self.do_normalize
            lowerCAmelCase    		=			image_mean if image_mean is not None else self.image_mean
            lowerCAmelCase    		=			image_std if image_std is not None else self.image_std

            lowerCAmelCase    		=			size if size is not None else self.size
            lowerCAmelCase    		=			get_size_dict(lowercase ,		default_to_square=lowercase					)
            lowerCAmelCase    		=			crop_size if crop_size is not None else self.crop_size
            lowerCAmelCase    		=			get_size_dict(lowercase ,		param_name="""crop_size"""					)

            lowerCAmelCase    		=			make_list_of_images(lowercase					)

            if not valid_images(lowercase					):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""					)

            if do_resize and size is None or resample is None:
                  raise ValueError("""Size and resample must be specified if do_resize is True."""					)

            if do_center_crop and crop_pct is None:
                  raise ValueError("""Crop_pct must be specified if do_center_crop is True."""					)

            if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

            if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

            # All transformations expect numpy arrays.
            lowerCAmelCase    		=			[to_numpy_array(lowercase					) for image in images]

            if do_resize:
                  lowerCAmelCase    		=			[self.resize(image=lowercase ,		size=lowercase ,		crop_pct=lowercase ,		resample=lowercase					) for image in images]

            if do_center_crop:
                  lowerCAmelCase    		=			[self.center_crop(image=lowercase ,		size=lowercase					) for image in images]

            if do_rescale:
                  lowerCAmelCase    		=			[self.rescale(image=lowercase ,		scale=lowercase					) for image in images]

            if do_normalize:
                  lowerCAmelCase    		=			[self.normalize(image=lowercase ,		mean=lowercase ,		std=lowercase					) for image in images]

            lowerCAmelCase    		=			[to_channel_dimension_format(lowercase ,		lowercase					) for image in images]

            lowerCAmelCase    		=			{"""pixel_values""": images}
            return BatchFeature(data=lowercase ,		tensor_type=lowercase					)













"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






